'''Trains a simple convnet on the MNIST dataset.
Gets to 99.25% test accuracy after 12 epochs
(there is still a lot of margin for parameter tuning).
16 seconds per epoch on a GRID K520 GPU.
'''
from __future__ import print_function
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras import backend as K

def preprocess(data_set, class_num):
    (x_train,y_train),(x_test,y_test) = data_set
    w, h = 28, 28
    if K.image_data_format() == 'channels_first':
        x_train = x_train.reshape(x_train.shape[0], 1, h, w)
        x_test = x_test.reshape(x_test.shape[0], 1, h, w)
        input_shape = (1, h, w)
        print("(1,h,w)")
    else:
        x_train = x_train.reshape(x_train.shape[0], h, w, 1)
        x_test = x_test.reshape(x_test.shape[0], h, w, 1)
        input_shape = (h, w, 1)
        print("(h,w,1)")

    x_train = x_train.astype('float32')
    x_test = x_test.astype('float32')
    x_train /= 255
    x_test /= 255
    print('x_train shape:', x_train.shape)
    print(x_train.shape[0], 'train samples')
    print(x_test.shape[0], 'test samples')

    # convert class vectors to binary class matrices
    y_train = keras.utils.to_categorical(y_train, class_num)
    y_test = keras.utils.to_categorical(y_test, class_num)

    return (x_train, y_train, x_test, y_test, input_shape)

def create_cnn(input_shape, class_num):
    model = Sequential()
    model.add(Conv2D(32, kernel_size=(3, 3),
                    activation='relu',
                    input_shape=input_shape))
    model.add(Conv2D(64, (3, 3), activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.25))
    model.add(Flatten())
    model.add(Dense(128, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(class_num, activation='softmax'))

    model.compile(loss=keras.losses.categorical_crossentropy,
                optimizer=keras.optimizers.Adadelta(),
                metrics=['accuracy'])
    return model

if __name__ == '__main__':
    #preprocess data
    class_num = 10
    src = mnist.load_data()
    data = preprocess(src, class_num)
    (x_train,y_train,x_test,y_test,input_shape) = data

    #train model
    model = create_cnn(input_shape, class_num)
    model.fit(x_train, y_train, 
            batch_size=2000, epochs=20, verbose=1, validation_data=(x_test, y_test))
    print("x_train_shape")
    print(x_train.shape)
    print("x_test_shape")
    print(x_test.shape)

    #save
    model.save("mnist.h5")

    #print
    score = model.evaluate(x_test, y_test, verbose=0)
    print('Test loss:', score[0])
    print('Test accuracy:', score[1])